Animated Graphs
Load packages
# Load packages
pacman::p_load(tidyverse, lubridate, ggthemes, ggrepel, cansim, janitor, gganimate)Access Statistics Canada data
gdp_can_sample <- get_cansim(36100222) %>% filter(GEO == "Canada" & REF_DATE == 2019) %>%
select(REF_DATE, VALUE, Prices, Estimates)
gdp <- get_cansim(36100222) %>% normalize_cansim_values()
glimpse(gdp)## Rows: 101,280
## Columns: 22
## $ REF_DATE <chr> "1981", "1981", "1981", "1981", "1~
## $ GEO <chr> "Canada", "Canada", "Canada", "Can~
## $ DGUID <chr> "2016A000011124", "2016A000011124"~
## $ UOM <chr> "Dollars", "Dollars", "Dollars", "~
## $ UOM_ID <chr> "81", "81", "81", "81", "81", "81"~
## $ VECTOR <chr> "v62787252", "v62787253", "v627872~
## $ COORDINATE <chr> "1.1.1", "1.1.2", "1.1.3", "1.1.4"~
## $ VALUE <dbl> 657758000000, 429530000000, 213179~
## $ STATUS <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ SYMBOL <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ TERMINATED <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ DECIMALS <chr> "0", "0", "0", "0", "0", "0", "0",~
## $ GeoUID <chr> "11124", "11124", "11124", "11124"~
## $ `Hierarchy for GEO` <chr> "1", "1", "1", "1", "1", "1", "1",~
## $ `Classification Code for Prices` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ `Hierarchy for Prices` <chr> "1", "1", "1", "1", "1", "1", "1",~
## $ `Classification Code for Estimates` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ `Hierarchy for Estimates` <chr> "1", "1.2", "1.2.3", "1.2.3.4", "1~
## $ val_norm <dbl> 657758000000, 429530000000, 213179~
## $ Date <date> 1981-01-01, 1981-01-01, 1981-01-0~
## $ Prices <fct> Chained (2012) dollars, Chained (2~
## $ Estimates <fct> "Final consumption expenditure", "~Create function to create new variable called geo_short
provlist <- c("British Columbia","Alberta","Saskatchewan","Manitoba","Ontario",
"Quebec", "New Brunswick","Nova Scotia","Prince Edward Island",
"Newfoundland and Labrador")
short_provs <- function(df){
df <- df %>%
filter(GEO %in% provlist) %>%
mutate(geo_short = case_when(
GEO=="British Columbia" ~ "BC",
GEO=="Alberta" ~ "AB",
GEO=="Saskatchewan" ~ "SK",
GEO=="Manitoba" ~ "MB",
GEO=="Ontario" ~ "ON",
GEO=="Quebec" ~ "QC",
GEO=="New Brunswick" ~ "NB",
GEO=="Prince Edward Island" ~ "PE",
GEO=="Nova Scotia" ~ "NS",
GEO=="Newfoundland and Labrador" ~ "NL"
))
df$geo_short <- factor(df$geo_short,
levels=c("BC","AB","SK","MB","ON",
"QC","NB","PE","NS","NL"))
df
}Do some data cleaning
df_gdp <- gdp %>%
mutate(year=as.integer(REF_DATE)) %>%
filter(GEO %in% provlist,
Prices=="Chained (2012) dollars",
Estimates=="Gross domestic product at market prices") %>%
select(GEO, year, gdp = VALUE)
df_gdp <- short_provs(df_gdp) %>%
clean_names()
head(df_gdp)## # A tibble: 6 x 4
## geo year gdp geo_short
## <chr> <int> <dbl> <fct>
## 1 Newfoundland and Labrador 1981 15452000000 NL
## 2 Prince Edward Island 1981 2639000000 PE
## 3 Nova Scotia 1981 20616000000 NS
## 4 New Brunswick 1981 16898000000 NB
## 5 Quebec 1981 196091000000 QC
## 6 Ontario 1981 308001000000 ONLet’s start plotting!
plot_gdp <- ggplot(df_gdp, aes(geo_short, gdp/1e9, fill = geo_short, label = geo_short)) +
geom_col()
plot_gdp
Let’s add some additional details
plot_gdp <- plot_gdp +
geom_text(vjust = -0.1) +
scale_fill_viridis_d() +
scale_y_continuous(labels=scales::dollar) +
theme_tufte(base_size = 12) +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Year: {frame_time}", x = "", y = "GDP (2012$ billions)")
plot_gdp
Let’s add some animation
plot_gdp +
transition_time(year)
If you want a static graph
df_gdp %>% #<<
group_by(geo_short) %>% #<<
summarize(gdp_plot = mean(gdp/1e9)) %>%
ggplot(aes(geo_short, gdp_plot, fill = geo_short, label = geo_short)) +
geom_col() +
geom_text(vjust = -0.1, size = 3) +
scale_fill_viridis_d() +
scale_y_continuous(labels=scales::dollar) +
theme_tufte(base_size = 10) +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Average GDP by province", x = "", y = "GDP (2012$ billions)")
But provinces have different sizes, so let’s do GDP / capita instead
pop <- get_cansim(1710000501) %>% normalize_cansim_values()
df_pop <- pop %>%
mutate(year = as.integer(REF_DATE)) %>%
filter(GEO %in% provlist,
Sex=="Both sexes",
`Age group`=="All ages") %>%
dplyr::select(GEO, year, pop=VALUE) %>%
clean_names()
df_gdpcap <-left_join(df_gdp,df_pop, by=c("year","geo")) %>%
mutate(gdpcap=gdp/pop) Let’s graph GDP/capita
p_gdpcap <- ggplot(df_gdpcap, aes(geo_short, gdpcap,
fill = geo_short, label = geo_short)) +
geom_col() +
geom_text(vjust = -0.1, check_overlap = T) +
scale_fill_viridis_d(name="") +
scale_y_continuous(labels=scales::dollar) +
theme_tufte(base_size = 12) +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
labs(title = 'Year: {frame_time}', x = '', y = 'GDP per capita ($2012)') +
transition_time(year) +
ease_aes('linear')
animate(p_gdpcap, fps=5, end_pause = 30)
Let’s make it extra cool. What if we get the provinces to rank themselves every single frame? Let’s first create a ranking variable.
df_ordered <- df_gdpcap %>%
group_by(year) %>%
mutate(prov_order = rank(gdpcap))Let’s plot it now. Use geom_tile instead of geom_col because it works better with the frame transitions
p_rank <- ggplot(df_ordered,
aes(prov_order, group = geo_short, color= geo_short, fill=geo_short)) +
geom_tile(aes(y = gdpcap/2,
height = gdpcap,
width = 0.9), alpha = 0.9) +
# text on top of bars
geom_text(aes(y = gdpcap, label = geo_short), hjust = -0.4) +
# text in x-axis (requires clip = "off" in coord_cartesian)
geom_text(aes(y = 0, label = geo_short), hjust = 1.4) +
coord_flip(clip = "off", expand = FALSE) +
scale_color_viridis_d(name="")+
scale_fill_viridis_d(name="")+
scale_y_continuous(labels=scales::dollar)+
theme_tufte(14)+
guides(color=F,fill=F)+
labs(title='{frame_time}', x = "",y="GDP per capita ($2012)") +
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.y = element_blank(),
axis.text.y = element_blank()) +
transition_time(year)+
ease_aes('cubic-in-out')
animate(p_rank, nframes = 100, fps = 5, end_pause = 20) 